"""simple docstring"""


import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				# to overwrite at feature extractactor specific tests
				A__ :  List[str]    				=			None
				A__ :  int    				=			None

				@property
				def 						_a      ( self ):
										return self.feat_extract_tester.prepare_feat_extract_dict()

				def 						_a      ( self ):
										lowerCamelCase__   =self.feature_extraction_class(**self.feat_extract_dict )
										self.assertTrue(hasattr(_lowerCamelCase      ,					"feature_size" ) )
										self.assertTrue(hasattr(_lowerCamelCase      ,					"sampling_rate" ) )
										self.assertTrue(hasattr(_lowerCamelCase      ,					"padding_value" ) )

				def 						_a      ( self ):
										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common()
										lowerCamelCase__   =self.feature_extraction_class(**self.feat_extract_dict )
										lowerCamelCase__   =feat_extract.model_input_names[0]

										lowerCamelCase__   =BatchFeature({input_name: speech_inputs} )

										self.assertTrue(all(len(_lowerCamelCase ) == len(_lowerCamelCase ) for x, y in zip(_lowerCamelCase      ,					processed_features[input_name] ) ) )

										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase )
										lowerCamelCase__   =BatchFeature({input_name: speech_inputs}      ,					tensor_type="np" )

										lowerCamelCase__   =processed_features[input_name]

										if len(batch_features_input.shape ) < 3:
																lowerCamelCase__   =batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )

				@require_torch
				def 						_a      ( self ):
										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase )
										lowerCamelCase__   =self.feature_extraction_class(**self.feat_extract_dict )
										lowerCamelCase__   =feat_extract.model_input_names[0]

										lowerCamelCase__   =BatchFeature({input_name: speech_inputs}      ,					tensor_type="pt" )

										lowerCamelCase__   =processed_features[input_name]

										if len(batch_features_input.shape ) < 3:
																lowerCamelCase__   =batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )

				@require_tf
				def 						_a      ( self ):
										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase )
										lowerCamelCase__   =self.feature_extraction_class(**self.feat_extract_dict )
										lowerCamelCase__   =feat_extract.model_input_names[0]

										lowerCamelCase__   =BatchFeature({input_name: speech_inputs}      ,					tensor_type="tf" )

										lowerCamelCase__   =processed_features[input_name]

										if len(batch_features_input.shape ) < 3:
																lowerCamelCase__   =batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )

				def 						_a      ( self      ,					_lowerCamelCase=False ):
										def _inputs_have_equal_length(_lowerCamelCase ):
																lowerCamelCase__   =len(input[0] )
																for input_slice in input[1:]:
																						if len(_lowerCamelCase ) != length:
																												return False
																return True

										def _inputs_are_equal(_lowerCamelCase      ,					_lowerCamelCase ):
																if len(_lowerCamelCase ) != len(_lowerCamelCase ):
																						return False

																for input_slice_a, input_slice_a in zip(_lowerCamelCase      ,					_lowerCamelCase ):
																						if not np.allclose(np.asarray(_lowerCamelCase )      ,					np.asarray(_lowerCamelCase )      ,					atol=1E-3 ):
																												return False
																return True

										lowerCamelCase__   =self.feature_extraction_class(**self.feat_extract_dict )
										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase )
										lowerCamelCase__   =feat_extract.model_input_names[0]

										lowerCamelCase__   =BatchFeature({input_name: speech_inputs} )

										lowerCamelCase__   =self.feat_extract_tester.seq_length_diff
										lowerCamelCase__   =self.feat_extract_tester.max_seq_length + pad_diff
										lowerCamelCase__   =self.feat_extract_tester.min_seq_length
										lowerCamelCase__   =self.feat_extract_tester.batch_size
										lowerCamelCase__   =self.feat_extract_tester.feature_size

										# test padding for List[int] + numpy
										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding=_lowerCamelCase )
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="longest" )
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[-1] ) )
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="longest"      ,					return_tensors="np" )
										lowerCamelCase__   =input_a[input_name]

										# max_length parameter has to be provided when setting `padding="max_length"`
										with self.assertRaises(_lowerCamelCase ):
																feat_extract.pad(_lowerCamelCase      ,					padding="max_length" )[input_name]

										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=_lowerCamelCase      ,					return_tensors="np" )
										lowerCamelCase__   =input_a[input_name]

										self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
										self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
										self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
										self.assertTrue(_inputs_are_equal(_lowerCamelCase      ,					_lowerCamelCase ) )
										self.assertTrue(len(input_a[0] ) == pad_min_length )
										self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
										self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
										self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )

										if feature_size > 1:
																self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )

										# test padding for `pad_to_multiple_of` for List[int] + numpy
										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					pad_to_multiple_of=10 )
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="longest"      ,					pad_to_multiple_of=10 )
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					pad_to_multiple_of=10      ,					max_length=_lowerCamelCase )
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					pad_to_multiple_of=10      ,					max_length=_lowerCamelCase      ,					return_tensors="np"      ,					)
										lowerCamelCase__   =input_a[input_name]

										self.assertTrue(all(len(_lowerCamelCase ) % 10 == 0 for x in input_a ) )
										self.assertTrue(_inputs_are_equal(_lowerCamelCase      ,					_lowerCamelCase ) )

										lowerCamelCase__   =pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
										self.assertTrue(all(len(_lowerCamelCase ) == expected_mult_pad_length for x in input_a ) )
										self.assertEqual(input_a.shape[:2]      ,					(batch_size, expected_mult_pad_length) )

										if feature_size > 1:
																self.assertTrue(input_a.shape[2] == feature_size )

										# Check padding value is correct
										lowerCamelCase__   =(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
										self.assertTrue(
										    abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
										    < 1E-3 )
										self.assertTrue(
										    abs(
										        np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
										        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
										    < 1E-3 )
										self.assertTrue(
										    abs(
										        np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
										        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
										    < 1E-3 )
										self.assertTrue(
										    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
										self.assertTrue(
										    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
										    < 1E-3 )

				def 						_a      ( self      ,					_lowerCamelCase=False ):
										def _inputs_have_equal_length(_lowerCamelCase ):
																lowerCamelCase__   =len(input[0] )
																for input_slice in input[1:]:
																						if len(_lowerCamelCase ) != length:
																												return False
																return True

										def _inputs_are_equal(_lowerCamelCase      ,					_lowerCamelCase ):
																if len(_lowerCamelCase ) != len(_lowerCamelCase ):
																						return False

																for input_slice_a, input_slice_a in zip(_lowerCamelCase      ,					_lowerCamelCase ):
																						if not np.allclose(np.asarray(_lowerCamelCase )      ,					np.asarray(_lowerCamelCase )      ,					atol=1E-3 ):
																												return False
																return True

										lowerCamelCase__   =self.feature_extraction_class(**self.feat_extract_dict )
										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase )
										lowerCamelCase__   =feat_extract.model_input_names[0]

										lowerCamelCase__   =BatchFeature({input_name: speech_inputs} )

										# truncate to smallest
										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[0] )      ,					truncation=_lowerCamelCase )
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[0] ) )
										lowerCamelCase__   =input_a[input_name]

										self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
										self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )

										# truncate to smallest with np
										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[0] )      ,					return_tensors="np"      ,					truncation=_lowerCamelCase      ,					)
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[0] )      ,					return_tensors="np" )
										lowerCamelCase__   =input_a[input_name]

										self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
										self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )

										# since truncation forces padding to be smaller than longest input
										# function can't return `np.ndarray`, but has to return list
										self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )

										# truncate to middle
										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[1] )      ,					truncation=_lowerCamelCase      ,					return_tensors="np"      ,					)
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[1] )      ,					truncation=_lowerCamelCase )
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[1] )      ,					return_tensors="np" )
										lowerCamelCase__   =input_a[input_name]

										self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
										self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
										self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
										self.assertTrue(_inputs_are_equal(_lowerCamelCase      ,					_lowerCamelCase ) )

										# since truncation forces padding to be smaller than longest input
										# function can't return `np.ndarray`, but has to return list
										self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
										self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

										# padding has to be max_length when setting `truncation=True`
										with self.assertRaises(_lowerCamelCase ):
																feat_extract.pad(_lowerCamelCase      ,					truncation=_lowerCamelCase )[input_name]

										# padding has to be max_length when setting `truncation=True`
										with self.assertRaises(_lowerCamelCase ):
																feat_extract.pad(_lowerCamelCase      ,					padding="longest"      ,					truncation=_lowerCamelCase )[input_name]

										# padding has to be max_length when setting `truncation=True`
										with self.assertRaises(_lowerCamelCase ):
																feat_extract.pad(_lowerCamelCase      ,					padding="longest"      ,					truncation=_lowerCamelCase )[input_name]

										# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
										with self.assertRaises(_lowerCamelCase ):
																feat_extract.pad(_lowerCamelCase      ,					padding="max_length"      ,					truncation=_lowerCamelCase )[input_name]

										# test truncation for `pad_to_multiple_of` for List[int] + numpy
										lowerCamelCase__   =12
										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[0] )      ,					pad_to_multiple_of=_lowerCamelCase      ,					truncation=_lowerCamelCase      ,					)
										lowerCamelCase__   =input_a[input_name]

										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=len(speech_inputs[0] )      ,					pad_to_multiple_of=_lowerCamelCase      ,					)
										lowerCamelCase__   =input_a[input_name]

										# retrieve expected_length as multiple of pad_to_multiple_of
										lowerCamelCase__   =len(speech_inputs[0] )
										if expected_length % pad_to_multiple_of != 0:
																lowerCamelCase__   =((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

										self.assertTrue(len(input_a[0] ) == expected_length )
										self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
										self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )

				def 						_a      ( self ):
										self._check_padding(numpify=_lowerCamelCase )

				def 						_a      ( self ):
										self._check_padding(numpify=_lowerCamelCase )

				def 						_a      ( self ):
										self._check_truncation(numpify=_lowerCamelCase )

				def 						_a      ( self ):
										self._check_truncation(numpify=_lowerCamelCase )

				@require_torch
				def 						_a      ( self ):
										lowerCamelCase__   =self.feature_extraction_class(**self.feat_extract_dict )
										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common()
										lowerCamelCase__   =feat_extract.model_input_names[0]

										lowerCamelCase__   =BatchFeature({input_name: speech_inputs} )

										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="longest"      ,					return_tensors="np" )[input_name]
										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="longest"      ,					return_tensors="pt" )[input_name]

										self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )

				@require_tf
				def 						_a      ( self ):
										lowerCamelCase__   =self.feature_extraction_class(**self.feat_extract_dict )
										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common()
										lowerCamelCase__   =feat_extract.model_input_names[0]

										lowerCamelCase__   =BatchFeature({input_name: speech_inputs} )

										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="longest"      ,					return_tensors="np" )[input_name]
										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="longest"      ,					return_tensors="tf" )[input_name]

										self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )

				def 						_a      ( self ):
										lowerCamelCase__   =self.feat_extract_dict
										lowerCamelCase__   =True
										lowerCamelCase__   =self.feature_extraction_class(**_lowerCamelCase )
										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common()
										lowerCamelCase__   =[len(_lowerCamelCase ) for x in speech_inputs]
										lowerCamelCase__   =feat_extract.model_input_names[0]

										lowerCamelCase__   =BatchFeature({input_name: speech_inputs} )

										lowerCamelCase__   =feat_extract.pad(_lowerCamelCase      ,					padding="longest"      ,					return_tensors="np" )
										self.assertIn("attention_mask"      ,					_lowerCamelCase )
										self.assertListEqual(list(processed.attention_mask.shape )      ,					list(processed[input_name].shape[:2] ) )
										self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()      ,					_lowerCamelCase )






				def 						_a      ( self ):
										lowerCamelCase__   =self.feat_extract_dict
										lowerCamelCase__   =True
										lowerCamelCase__   =self.feature_extraction_class(**_lowerCamelCase )
										lowerCamelCase__   =self.feat_extract_tester.prepare_inputs_for_common()
										lowerCamelCase__   =[len(_lowerCamelCase ) for x in speech_inputs]
										lowerCamelCase__   =feat_extract.model_input_names[0]

										lowerCamelCase__   =BatchFeature({input_name: speech_inputs} )
										lowerCamelCase__   =min(_lowerCamelCase )

										lowerCamelCase__   =feat_extract.pad(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=_lowerCamelCase      ,					truncation=_lowerCamelCase      ,					return_tensors="np" )
										self.assertIn("attention_mask"      ,					_lowerCamelCase )
										self.assertListEqual(
										    list(processed_pad.attention_mask.shape )      ,					[processed_pad[input_name].shape[0], max_length] )
										self.assertListEqual(
										    processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()      ,					[max_length for x in speech_inputs] )



"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  List[Any]    				=			'''Wav2Vec2FeatureExtractor'''
				A__ :  Any    				=			'''AutoTokenizer'''

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										super().__init__(_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False

				@classmethod
				def 						_a      ( cls      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
										try:
																return super().from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
										except OSError:
																warnings.warn(
																    F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
																    " include a `tokenizer_class` attribute is deprecated and will be "
																    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
																    " attribute to either your `config.json` or `tokenizer_config.json` "
																    "file to suppress this warning: "      ,					_lowerCamelCase      ,					)

																lowerCamelCase__   =WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
																lowerCamelCase__   =WavaVecaCTCTokenizer.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )

																return cls(feature_extractor=_lowerCamelCase      ,					tokenizer=_lowerCamelCase )

				def __call__( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor(*_lowerCamelCase      ,					**_lowerCamelCase )

										if "raw_speech" in kwargs:
																warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
																lowerCamelCase__   =kwargs.pop("raw_speech" )
										else:
																lowerCamelCase__   =kwargs.pop("audio"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("sampling_rate"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("text"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if audio is None and text is None:
																raise ValueError("You need to specify either an `audio` or `text` input to process." )

										if audio is not None:
																lowerCamelCase__   =self.feature_extractor(_lowerCamelCase      ,					*_lowerCamelCase      ,					sampling_rate=_lowerCamelCase      ,					**_lowerCamelCase )
										if text is not None:
																lowerCamelCase__   =self.tokenizer(_lowerCamelCase      ,					**_lowerCamelCase )

										if text is None:
																return inputs
										elif audio is None:
																return encodings
										else:
																lowerCamelCase__   =encodings["input_ids"]
																return inputs

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor.pad(*_lowerCamelCase      ,					**_lowerCamelCase )

										lowerCamelCase__   =kwargs.pop("input_features"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("labels"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if input_features is not None:
																lowerCamelCase__   =self.feature_extractor.pad(_lowerCamelCase      ,					*_lowerCamelCase      ,					**_lowerCamelCase )
										if labels is not None:
																lowerCamelCase__   =self.tokenizer.pad(_lowerCamelCase      ,					**_lowerCamelCase )

										if labels is None:
																return input_features
										elif input_features is None:
																return labels
										else:
																lowerCamelCase__   =labels["input_ids"]
																return input_features

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.batch_decode(*_lowerCamelCase      ,					**_lowerCamelCase )

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.decode(*_lowerCamelCase      ,					**_lowerCamelCase )






				@contextmanager
				def 						_a      ( self ):
										warnings.warn(
										    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
										    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
										    "your audio inputs, or in a separate call." )
										lowerCamelCase__   =True
										lowerCamelCase__   =self.tokenizer
										yield
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False


